We analyze the impacts of alternative submarket definitions when predicting house prices in a mass appraisal context, using both ordinary least squares (OLS) and geostatistical techniques. For this purpose, we use over 13,000 housing transactions for Louisville, Kentucky. We use districts...

The measurement of house price movements is a vital topic from both academic and practical perspectives and hence has been the focus of much research. There is almost unanimous consensus in the literature that house price indexes should control for the quality of properties; the most widely used...

This paper tests whether out-of-sample hedonic value predictions can be improved when a large urban housing market is divided into submarkets.

In this paper, it is examined whether the structure of constrained submarkets constructed according to three a priori classifications (property type, house value and geographical areas) differs from that of unconstrained submarkets constructed by means of principal component analysis and cluster...

In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a "black box". An important example is hedonic property valuation modeling, where machine learning techniques...

Purpose: The purpose of this paper is to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models. Design/methodology/approach: The authors apply six estimation methods (linear least squares, robust regression, mixed-effects regression,...